UniMAP Library Digital Repository
Not a member yet
77924 research outputs found
Sort by
Investigation of factors influencing customer loyalty in Malaysia and Jordan hotel industry
No full textMaster of Science in ManagementThe ever increasing establishment of various star hotels in Malaysia and Jordan has leverage the choice options of guests and tourist in these countries. While the guests were been offered multiple choices, the management of hotels are staggering to keep their existing guests and attract new ones through service provision to mitigate effects of competition. This study aims to proffer solutions to customer loyalty challenges at these destinations by proposing and validating customer loyalty model using relationship marketing and social exchange theory. Based on the underpinning theories, this study analyzed the direct and indirect influence of brand image, trust, convenience and emotion on customer loyalty of five star hotels in Malaysia and Jordan. Further, the mediating effect of customer satisfaction on the relationship between exogenous latent variables and endogenous latent variable was investigated. A total of 384 and 371customers respectively of three famous hotels under the management of Starwood were sampled
using convenience sampling method for data analysis from Malaysia and Jordan. SPSS
version 21.0 software was used for the analysis. The results of empirical analysis
supported all the hypothesized relationships. In Malaysia, customers considered brand
image and convenience as the most significant influencer of customer loyalty. While
Jordanian 5 star hotels customers considered emotion, brand image and convenience as
the most significant factors of customer loyalty. However, the empirical results showed
partial mediation effects on the relationship between brand image, trust, convenience,
emotion and customer loyalty in respect of Malaysian hotel customers. In Jordan hotels,
satisfaction does not mediate the relationship between trust and customer loyalty but
partially mediate between emotion, brand image, convenience and customer loyalty. In
summary, the findings of this study will narrow the perception of the top echelon of these
hotels on the actual factors to focus in order to earn loyalty of their valued customers.
This study also contributed to frontier of knowledge by integrating the variables of
relationship marketing from the perspectives of two developing countries. The study
made useful recommendations that will benefit hotel industry generally and suggest
direction for further research
Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
No full textDoctor of Philosophy in Mechatronic EngineeringAuscultation is the process of listening to the internal sounds of the body using a stethoscope. This process provides vital information on the present state of the internal organs, such as the heart, lungs and the gastrointestinal system. Auscultation is subjective and prone to be not reliable. However computerized respiratory sound analysis is more effective and reliable. This
thesis discusses the development of a computerized decision support system (CDSS) to detect respiratory pathology using pulmonary acoustic signals. The pulmonary acoustics signals were collected from 72 subjects to develop the CDSS. In order to develop the CDSS tool, three different methodological frameworks were proposed to determine the most effective classification of respiratory pathology. The recorded pulmonary acoustics signals were filtered to remove noise and other artifacts followed by respiratory cycle segmentation. In this work, the respiratory cycle segmentation is performed by using Fuzzy Inference system. Parametric (Mel-frequency cepstral coefficients (MFCC) and Auto-regressive model (AR)) and Nonparametric
(wavelet packet transform (WPT) and Stockwell transform (ST)) representations of features were extracted. The features extracted were dimensionally reduced using principal component analysis and a statistical analysis was performed to determine the significance level of the feature vector using One-way ANOVA. Observations showed that the extracted features were statistically significant with p < 0.05. In the classification stage various nonlinear classifiers such as k-nearest neighbor (k-nn), support vector machines (SVM) and extreme learning machine (ELM) were implemented to classify the respiratory pathology from respiratory sounds. In the classification, extreme learning machine performed better than k-nn and support vector machine classifier for all the frameworks. Experimental results showed that ST based feature extraction performed well with ELM classifier with third framework. The ST based features and ELM classifier with third framework was validated using a new set of data
comprising of 48 subjects and the system was found to be reliable with mean classification
accuracy of 96.63%, 97.57% and 98.48% for classifying (normal, continuous lung sounds and
discontinuous lung sounds), (wheeze and rhonchi) and (fine crackles and coarse crackles)
respectively. After successful validation a CDSS tool was developed using the ST based
features and ELM classifier with third framewor